Overview

Dataset statistics

Number of variables25
Number of observations29886
Missing cells149739
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory200.0 B

Variable types

Categorical9
Numeric16

Alerts

Date has a high cardinality: 879 distinct valuesHigh cardinality
Administered_Dose1 has a high cardinality: 27840 distinct valuesHigh cardinality
Series_Complete_Yes has a high cardinality: 27369 distinct valuesHigh cardinality
Booster_Doses_Yes has a high cardinality: 17882 distinct valuesHigh cardinality
Booster_Doses_Yes_Last14Days has a high cardinality: 17129 distinct valuesHigh cardinality
Second_Booster has a high cardinality: 12233 distinct valuesHigh cardinality
Second_Booster_Last14Days has a high cardinality: 11189 distinct valuesHigh cardinality
Bivalent_Booster has a high cardinality: 7604 distinct valuesHigh cardinality
Administered_Dose1_pct_known is highly overall correlated with Administered_Dose1_pct_US and 12 other fieldsHigh correlation
Administered_Dose1_pct_US is highly overall correlated with Administered_Dose1_pct_known and 12 other fieldsHigh correlation
Administered_Dose1_pct_agegroup is highly overall correlated with Administered_Dose1_pct_known and 15 other fieldsHigh correlation
Series_Complete_Pop_pct_agegroup is highly overall correlated with Administered_Dose1_pct_known and 15 other fieldsHigh correlation
Series_Complete_Pop_Pct_known is highly overall correlated with Administered_Dose1_pct_known and 12 other fieldsHigh correlation
Series_Complete_Pop_Pct_US is highly overall correlated with Administered_Dose1_pct_known and 12 other fieldsHigh correlation
Booster_Doses_Vax_pct_agegroup is highly overall correlated with Administered_Dose1_pct_agegroup and 9 other fieldsHigh correlation
Booster_Doses_Pop_Pct_known is highly overall correlated with Administered_Dose1_pct_known and 15 other fieldsHigh correlation
Booster_Doses_Vax_Pct_US is highly overall correlated with Administered_Dose1_pct_known and 12 other fieldsHigh correlation
Booster_Doses_Pop_Pct_known_Last14Days is highly overall correlated with Administered_Dose1_pct_known and 14 other fieldsHigh correlation
Second_Booster_Vax_pct_agegroup is highly overall correlated with Administered_Dose1_pct_agegroup and 8 other fieldsHigh correlation
Second_Booster_Pop_Pct_known is highly overall correlated with Administered_Dose1_pct_known and 14 other fieldsHigh correlation
Second_Booster_Pop_Pct_US is highly overall correlated with Administered_Dose1_pct_known and 14 other fieldsHigh correlation
Second_Booster_Pop_Pct_known_Last14Days is highly overall correlated with Administered_Dose1_pct_known and 14 other fieldsHigh correlation
Bivalent_Booster_Pop_Pct_agegroup is highly overall correlated with Administered_Dose1_pct_agegroup and 6 other fieldsHigh correlation
Bivalent_Booster_Pop_Pct_known is highly overall correlated with Administered_Dose1_pct_known and 14 other fieldsHigh correlation
Demographic_category is highly overall correlated with Administered_Dose1_pct_known and 13 other fieldsHigh correlation
Administered_Dose1_pct_known has 879 (2.9%) missing valuesMissing
Administered_Dose1_pct_agegroup has 7032 (23.5%) missing valuesMissing
Series_Complete_Pop_pct_agegroup has 7032 (23.5%) missing valuesMissing
Series_Complete_Pop_Pct_known has 879 (2.9%) missing valuesMissing
Booster_Doses_Pop_Pct_known has 879 (2.9%) missing valuesMissing
Booster_Doses_Pop_Pct_known_Last14Days has 879 (2.9%) missing valuesMissing
Second_Booster_Vax_pct_agegroup has 10548 (35.3%) missing valuesMissing
Second_Booster_Pop_Pct_known has 11427 (38.2%) missing valuesMissing
Second_Booster_Pop_Pct_US has 10548 (35.3%) missing valuesMissing
Second_Booster_Pop_Pct_known_Last14Days has 11427 (38.2%) missing valuesMissing
Second_Booster has 10548 (35.3%) missing valuesMissing
Second_Booster_Last14Days has 10548 (35.3%) missing valuesMissing
Bivalent_Booster has 21615 (72.3%) missing valuesMissing
Bivalent_Booster_Pop_Pct_agegroup has 23631 (79.1%) missing valuesMissing
Bivalent_Booster_Pop_Pct_known has 21867 (73.2%) missing valuesMissing
Date is uniformly distributedUniform
Demographic_category is uniformly distributedUniform
Administered_Dose1_pct_known has 2994 (10.0%) zerosZeros
Administered_Dose1_pct_US has 2994 (10.0%) zerosZeros
Administered_Dose1_pct_agegroup has 2133 (7.1%) zerosZeros
Series_Complete_Pop_pct_agegroup has 2750 (9.2%) zerosZeros
Series_Complete_Pop_Pct_known has 3196 (10.7%) zerosZeros
Series_Complete_Pop_Pct_US has 3196 (10.7%) zerosZeros
Booster_Doses_Vax_pct_agegroup has 11220 (37.5%) zerosZeros
Booster_Doses_Pop_Pct_known has 11384 (38.1%) zerosZeros
Booster_Doses_Vax_Pct_US has 5184 (17.3%) zerosZeros
Booster_Doses_Pop_Pct_known_Last14Days has 11041 (36.9%) zerosZeros
Second_Booster_Vax_pct_agegroup has 6030 (20.2%) zerosZeros
Second_Booster_Pop_Pct_known has 5772 (19.3%) zerosZeros
Second_Booster_Pop_Pct_US has 5772 (19.3%) zerosZeros
Second_Booster_Pop_Pct_known_Last14Days has 5818 (19.5%) zerosZeros
Bivalent_Booster_Pop_Pct_known has 502 (1.7%) zerosZeros

Reproduction

Analysis started2023-09-18 22:14:26.509241
Analysis finished2023-09-18 22:15:57.078193
Duration1 minute and 30.57 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct879
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
5/10/23
 
34
9/27/21
 
34
10/8/21
 
34
10/7/21
 
34
10/6/21
 
34
Other values (874)
29716 

Length

Max length8
Median length7
Mean length6.9340159
Min length6

Characters and Unicode

Total characters207230
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5/10/23
2nd row5/10/23
3rd row5/10/23
4th row5/10/23
5th row5/10/23

Common Values

ValueCountFrequency (%)
5/10/23 34
 
0.1%
9/27/21 34
 
0.1%
10/8/21 34
 
0.1%
10/7/21 34
 
0.1%
10/6/21 34
 
0.1%
10/5/21 34
 
0.1%
10/4/21 34
 
0.1%
10/3/21 34
 
0.1%
10/2/21 34
 
0.1%
10/1/21 34
 
0.1%
Other values (869) 29546
98.9%

Length

2023-09-18T22:15:57.266215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5/10/23 34
 
0.1%
4/28/23 34
 
0.1%
4/15/23 34
 
0.1%
5/8/23 34
 
0.1%
5/7/23 34
 
0.1%
5/6/23 34
 
0.1%
5/5/23 34
 
0.1%
5/4/23 34
 
0.1%
5/3/23 34
 
0.1%
5/2/23 34
 
0.1%
Other values (869) 29546
98.9%

Most occurring characters

ValueCountFrequency (%)
2 60588
29.2%
/ 59772
28.8%
1 37808
18.2%
3 12002
 
5.8%
4 6018
 
2.9%
0 5610
 
2.7%
5 5406
 
2.6%
7 5066
 
2.4%
8 5066
 
2.4%
6 4998
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147458
71.2%
Other Punctuation 59772
28.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 60588
41.1%
1 37808
25.6%
3 12002
 
8.1%
4 6018
 
4.1%
0 5610
 
3.8%
5 5406
 
3.7%
7 5066
 
3.4%
8 5066
 
3.4%
6 4998
 
3.4%
9 4896
 
3.3%
Other Punctuation
ValueCountFrequency (%)
/ 59772
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 207230
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 60588
29.2%
/ 59772
28.8%
1 37808
18.2%
3 12002
 
5.8%
4 6018
 
2.9%
0 5610
 
2.7%
5 5406
 
2.6%
7 5066
 
2.4%
8 5066
 
2.4%
6 4998
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 60588
29.2%
/ 59772
28.8%
1 37808
18.2%
3 12002
 
5.8%
4 6018
 
2.9%
0 5610
 
2.7%
5 5406
 
2.6%
7 5066
 
2.4%
8 5066
 
2.4%
6 4998
 
2.4%

Demographic_category
Categorical

HIGH CORRELATION  UNIFORM 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
Race_eth_NHAIAN
 
879
Ages_75+_yrs
 
879
Race_eth_unknown
 
879
Race_eth_known
 
879
Sex_known
 
879
Other values (29)
25491 

Length

Max length22
Median length17
Mean length13.205882
Min length2

Characters and Unicode

Total characters394671
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRace_eth_NHAIAN
2nd rowAge_unknown
3rd rowRace_eth_NHAsian
4th rowRace_eth_NHMult_Oth
5th rowRace_eth_NHBlack

Common Values

ValueCountFrequency (%)
Race_eth_NHAIAN 879
 
2.9%
Ages_75+_yrs 879
 
2.9%
Race_eth_unknown 879
 
2.9%
Race_eth_known 879
 
2.9%
Sex_known 879
 
2.9%
Ages_65+_yrs 879
 
2.9%
Ages_25-49_yrs 879
 
2.9%
US 879
 
2.9%
Race_eth_Hispanic 879
 
2.9%
Age_unknown 879
 
2.9%
Other values (24) 21096
70.6%

Length

2023-09-18T22:15:58.086073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
race_eth_nhaian 879
 
2.9%
ages_65-74_yrs 879
 
2.9%
sex_female 879
 
2.9%
ages_<2yrs 879
 
2.9%
age_known 879
 
2.9%
ages_12-17_yrs 879
 
2.9%
ages_<12yrs 879
 
2.9%
race_eth_nhmultiracial 879
 
2.9%
ages_40-49_yrs 879
 
2.9%
ages_75+_yrs 879
 
2.9%
Other values (24) 21096
70.6%

Most occurring characters

ValueCountFrequency (%)
_ 50982
 
12.9%
e 43071
 
10.9%
s 29886
 
7.6%
A 18459
 
4.7%
a 15822
 
4.0%
r 15822
 
4.0%
g 15822
 
4.0%
n 14943
 
3.8%
y 14064
 
3.6%
t 14064
 
3.6%
Other values (36) 161736
41.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 225024
57.0%
Uppercase Letter 61530
 
15.6%
Connector Punctuation 50982
 
12.9%
Decimal Number 43071
 
10.9%
Dash Punctuation 9669
 
2.4%
Math Symbol 4395
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 43071
19.1%
s 29886
13.3%
a 15822
 
7.0%
r 15822
 
7.0%
g 15822
 
7.0%
n 14943
 
6.6%
y 14064
 
6.2%
t 14064
 
6.2%
c 12306
 
5.5%
h 12306
 
5.5%
Other values (9) 36918
16.4%
Uppercase Letter
ValueCountFrequency (%)
A 18459
30.0%
R 9669
15.7%
H 8790
14.3%
N 8790
14.3%
S 4395
 
7.1%
M 2637
 
4.3%
O 2637
 
4.3%
I 1758
 
2.9%
W 879
 
1.4%
U 879
 
1.4%
Other values (3) 2637
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 8790
20.4%
5 7911
18.4%
2 7032
16.3%
4 6153
14.3%
6 3516
 
8.2%
7 3516
 
8.2%
9 2637
 
6.1%
0 1758
 
4.1%
8 879
 
2.0%
3 879
 
2.0%
Math Symbol
ValueCountFrequency (%)
< 2637
60.0%
+ 1758
40.0%
Connector Punctuation
ValueCountFrequency (%)
_ 50982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 286554
72.6%
Common 108117
 
27.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 43071
15.0%
s 29886
 
10.4%
A 18459
 
6.4%
a 15822
 
5.5%
r 15822
 
5.5%
g 15822
 
5.5%
n 14943
 
5.2%
y 14064
 
4.9%
t 14064
 
4.9%
c 12306
 
4.3%
Other values (22) 92295
32.2%
Common
ValueCountFrequency (%)
_ 50982
47.2%
- 9669
 
8.9%
1 8790
 
8.1%
5 7911
 
7.3%
2 7032
 
6.5%
4 6153
 
5.7%
6 3516
 
3.3%
7 3516
 
3.3%
< 2637
 
2.4%
9 2637
 
2.4%
Other values (4) 5274
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 50982
 
12.9%
e 43071
 
10.9%
s 29886
 
7.6%
A 18459
 
4.7%
a 15822
 
4.0%
r 15822
 
4.0%
g 15822
 
4.0%
n 14943
 
3.8%
y 14064
 
3.6%
t 14064
 
3.6%
Other values (36) 161736
41.0%
Distinct27840
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
0
 
1656
9,344
 
29
9,332
 
25
9,336
 
24
9,343
 
20
Other values (27835)
28132 

Length

Max length11
Median length10
Mean length8.9393696
Min length1

Characters and Unicode

Total characters267162
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27678 ?
Unique (%)92.6%

Sample

1st row1,911,855
2nd row9,344
3rd row13,983,704
4th row12,665,103
5th row21,157,654

Common Values

ValueCountFrequency (%)
0 1656
 
5.5%
9,344 29
 
0.1%
9,332 25
 
0.1%
9,336 24
 
0.1%
9,343 20
 
0.1%
9,269 18
 
0.1%
9,334 14
 
< 0.1%
9,339 13
 
< 0.1%
9,277 12
 
< 0.1%
9,263 8
 
< 0.1%
Other values (27830) 28067
93.9%

Length

2023-09-18T22:15:58.370997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 1656
 
5.5%
9,344 29
 
0.1%
9,332 25
 
0.1%
9,336 24
 
0.1%
9,343 20
 
0.1%
9,269 18
 
0.1%
9,334 14
 
< 0.1%
9,339 13
 
< 0.1%
9,277 12
 
< 0.1%
9,263 8
 
< 0.1%
Other values (27830) 28067
93.9%

Most occurring characters

ValueCountFrequency (%)
, 52314
19.6%
1 28137
10.5%
2 24981
9.4%
3 21904
8.2%
5 21015
7.9%
0 20940
7.8%
6 20900
 
7.8%
4 19948
 
7.5%
9 19419
 
7.3%
8 19048
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 214848
80.4%
Other Punctuation 52314
 
19.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28137
13.1%
2 24981
11.6%
3 21904
10.2%
5 21015
9.8%
0 20940
9.7%
6 20900
9.7%
4 19948
9.3%
9 19419
9.0%
8 19048
8.9%
7 18556
8.6%
Other Punctuation
ValueCountFrequency (%)
, 52314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 267162
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 52314
19.6%
1 28137
10.5%
2 24981
9.4%
3 21904
8.2%
5 21015
7.9%
0 20940
7.8%
6 20900
 
7.8%
4 19948
 
7.5%
9 19419
 
7.3%
8 19048
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 267162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 52314
19.6%
1 28137
10.5%
2 24981
9.4%
3 21904
8.2%
5 21015
7.9%
0 20940
7.8%
6 20900
 
7.8%
4 19948
 
7.5%
9 19419
 
7.3%
8 19048
 
7.1%

Administered_Dose1_pct_known
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct648
Distinct (%)2.2%
Missing879
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean21.451312
Minimum0
Maximum99.9
Zeros2994
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:15:58.664619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.8
median8.8
Q327.1
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)25.3

Descriptive statistics

Standard deviation29.300562
Coefficient of variation (CV)1.36591
Kurtosis1.9109016
Mean21.451312
Median Absolute Deviation (MAD)8.5
Skewness1.7220994
Sum622238.2
Variance858.52291
MonotonicityNot monotonic
2023-09-18T22:15:58.988609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2994
 
10.0%
99.9 2637
 
8.8%
0.9 1449
 
4.8%
0.3 910
 
3.0%
2.4 745
 
2.5%
6.8 741
 
2.5%
0.1 631
 
2.1%
3.9 628
 
2.1%
4.4 548
 
1.8%
6.2 528
 
1.8%
Other values (638) 17196
57.5%
(Missing) 879
 
2.9%
ValueCountFrequency (%)
0 2994
10.0%
0.1 631
 
2.1%
0.2 344
 
1.2%
0.3 910
 
3.0%
0.4 153
 
0.5%
0.5 187
 
0.6%
0.6 71
 
0.2%
0.7 148
 
0.5%
0.8 46
 
0.2%
0.9 1449
4.8%
ValueCountFrequency (%)
99.9 2637
8.8%
73.2 1
 
< 0.1%
72.9 1
 
< 0.1%
66.8 9
 
< 0.1%
66.7 2
 
< 0.1%
66.6 4
 
< 0.1%
66.5 4
 
< 0.1%
66.4 3
 
< 0.1%
66.3 4
 
< 0.1%
66.2 3
 
< 0.1%

Administered_Dose1_pct_US
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct708
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.981818
Minimum0
Maximum100
Zeros2994
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:15:59.301579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.7
median7.8
Q325.7
95-th percentile99.9
Maximum100
Range100
Interquartile range (IQR)24

Descriptive statistics

Standard deviation29.899893
Coefficient of variation (CV)1.3602102
Kurtosis1.48116
Mean21.981818
Median Absolute Deviation (MAD)7.6
Skewness1.6256669
Sum656948.6
Variance894.0036
MonotonicityNot monotonic
2023-09-18T22:15:59.597684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2994
 
10.0%
0.2 1150
 
3.8%
100 879
 
2.9%
99.9 879
 
2.9%
0.7 829
 
2.8%
3 785
 
2.6%
0.9 738
 
2.5%
99.1 719
 
2.4%
2.4 644
 
2.2%
0.1 632
 
2.1%
Other values (698) 19637
65.7%
ValueCountFrequency (%)
0 2994
10.0%
0.1 632
 
2.1%
0.2 1150
 
3.8%
0.3 115
 
0.4%
0.4 141
 
0.5%
0.5 187
 
0.6%
0.6 157
 
0.5%
0.7 829
 
2.8%
0.8 25
 
0.1%
0.9 738
 
2.5%
ValueCountFrequency (%)
100 879
2.9%
99.9 879
2.9%
99.2 1
 
< 0.1%
99.1 719
2.4%
99 52
 
0.2%
98.9 21
 
0.1%
98.8 11
 
< 0.1%
98.7 11
 
< 0.1%
98.6 6
 
< 0.1%
98.5 5
 
< 0.1%
Distinct27369
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
0
 
1678
2,491
 
62
2,486
 
47
2,488
 
35
2,478
 
21
Other values (27364)
28043 

Length

Max length11
Median length10
Mean length8.6999933
Min length1

Characters and Unicode

Total characters260008
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27024 ?
Unique (%)90.4%

Sample

1st row1,588,653
2nd row2,491
3rd row12,609,000
4th row11,389,487
5th row18,545,870

Common Values

ValueCountFrequency (%)
0 1678
 
5.6%
2,491 62
 
0.2%
2,486 47
 
0.2%
2,488 35
 
0.1%
2,478 21
 
0.1%
2,490 21
 
0.1%
2,489 18
 
0.1%
2,471 16
 
0.1%
2,455 15
 
0.1%
2,467 14
 
< 0.1%
Other values (27359) 27959
93.6%

Length

2023-09-18T22:15:59.877156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 1678
 
5.6%
2,491 62
 
0.2%
2,486 47
 
0.2%
2,488 35
 
0.1%
2,478 21
 
0.1%
2,490 21
 
0.1%
2,489 18
 
0.1%
2,471 16
 
0.1%
2,455 15
 
0.1%
2,467 14
 
< 0.1%
Other values (27359) 27959
93.6%

Most occurring characters

ValueCountFrequency (%)
, 50650
19.5%
1 28296
10.9%
2 23186
8.9%
4 21297
8.2%
0 21079
8.1%
5 20693
8.0%
3 20205
 
7.8%
8 18912
 
7.3%
7 18625
 
7.2%
9 18572
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 209358
80.5%
Other Punctuation 50650
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28296
13.5%
2 23186
11.1%
4 21297
10.2%
0 21079
10.1%
5 20693
9.9%
3 20205
9.7%
8 18912
9.0%
7 18625
8.9%
9 18572
8.9%
6 18493
8.8%
Other Punctuation
ValueCountFrequency (%)
, 50650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 260008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 50650
19.5%
1 28296
10.9%
2 23186
8.9%
4 21297
8.2%
0 21079
8.1%
5 20693
8.0%
3 20205
 
7.8%
8 18912
 
7.3%
7 18625
 
7.2%
9 18572
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 50650
19.5%
1 28296
10.9%
2 23186
8.9%
4 21297
8.2%
0 21079
8.1%
5 20693
8.0%
3 20205
 
7.8%
8 18912
 
7.3%
7 18625
 
7.2%
9 18572
 
7.1%

Administered_Dose1_pct_agegroup
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct951
Distinct (%)4.2%
Missing7032
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean52.402428
Minimum0
Maximum95
Zeros2133
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:00.141454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123.3
median61.7
Q378.2
95-th percentile95
Maximum95
Range95
Interquartile range (IQR)54.9

Descriptive statistics

Standard deviation32.015266
Coefficient of variation (CV)0.61095005
Kurtosis-1.1630187
Mean52.402428
Median Absolute Deviation (MAD)21
Skewness-0.46239262
Sum1197605.1
Variance1024.9773
MonotonicityNot monotonic
2023-09-18T22:16:00.618377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2133
 
7.1%
95 1875
 
6.3%
0.7 162
 
0.5%
0.1 138
 
0.5%
0.5 137
 
0.5%
0.4 107
 
0.4%
56.9 101
 
0.3%
62.1 86
 
0.3%
70.2 80
 
0.3%
78.3 79
 
0.3%
Other values (941) 17956
60.1%
(Missing) 7032
 
23.5%
ValueCountFrequency (%)
0 2133
7.1%
0.1 138
 
0.5%
0.2 75
 
0.3%
0.3 72
 
0.2%
0.4 107
 
0.4%
0.5 137
 
0.5%
0.6 59
 
0.2%
0.7 162
 
0.5%
0.8 32
 
0.1%
0.9 27
 
0.1%
ValueCountFrequency (%)
95 1875
6.3%
94.9 11
 
< 0.1%
94.8 12
 
< 0.1%
94.7 10
 
< 0.1%
94.6 13
 
< 0.1%
94.5 11
 
< 0.1%
94.4 10
 
< 0.1%
94.3 11
 
< 0.1%
94.2 10
 
< 0.1%
94.1 11
 
< 0.1%

Series_Complete_Pop_pct_agegroup
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct951
Distinct (%)4.2%
Missing7032
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean45.390199
Minimum0
Maximum95
Zeros2750
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:00.899006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.6
median55.1
Q366.4
95-th percentile89.9
Maximum95
Range95
Interquartile range (IQR)51.8

Descriptive statistics

Standard deviation29.728406
Coefficient of variation (CV)0.6549521
Kurtosis-1.1718027
Mean45.390199
Median Absolute Deviation (MAD)17.05
Skewness-0.33255833
Sum1037347.6
Variance883.77813
MonotonicityNot monotonic
2023-09-18T22:16:01.179971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2750
 
9.2%
95 227
 
0.8%
0.3 225
 
0.8%
0.4 152
 
0.5%
65.2 149
 
0.5%
66.7 122
 
0.4%
51.9 115
 
0.4%
69.4 114
 
0.4%
0.1 110
 
0.4%
0.5 110
 
0.4%
Other values (941) 18780
62.8%
(Missing) 7032
 
23.5%
ValueCountFrequency (%)
0 2750
9.2%
0.1 110
 
0.4%
0.2 92
 
0.3%
0.3 225
 
0.8%
0.4 152
 
0.5%
0.5 110
 
0.4%
0.6 58
 
0.2%
0.7 44
 
0.1%
0.8 46
 
0.2%
0.9 39
 
0.1%
ValueCountFrequency (%)
95 227
0.8%
94.9 5
 
< 0.1%
94.8 5
 
< 0.1%
94.7 5
 
< 0.1%
94.6 16
 
0.1%
94.5 18
 
0.1%
94.4 23
 
0.1%
94.3 86
 
0.3%
94.2 39
 
0.1%
94.1 33
 
0.1%

Series_Complete_Pop_Pct_known
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct674
Distinct (%)2.3%
Missing879
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean21.282756
Minimum0
Maximum99.9
Zeros3196
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:01.473897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.4
median8.4
Q325.6
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)24.2

Descriptive statistics

Standard deviation29.341456
Coefficient of variation (CV)1.3786493
Kurtosis1.9356861
Mean21.282756
Median Absolute Deviation (MAD)8.3
Skewness1.735112
Sum617348.9
Variance860.92106
MonotonicityNot monotonic
2023-09-18T22:16:01.778407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3196
 
10.7%
99.9 2637
 
8.8%
0.3 1035
 
3.5%
6.8 899
 
3.0%
0.1 804
 
2.7%
2.4 791
 
2.6%
3.7 679
 
2.3%
0.7 647
 
2.2%
2.5 563
 
1.9%
6.2 555
 
1.9%
Other values (664) 17201
57.6%
(Missing) 879
 
2.9%
ValueCountFrequency (%)
0 3196
10.7%
0.1 804
 
2.7%
0.2 263
 
0.9%
0.3 1035
 
3.5%
0.4 143
 
0.5%
0.5 82
 
0.3%
0.6 11
 
< 0.1%
0.7 647
 
2.2%
0.8 467
 
1.6%
0.9 441
 
1.5%
ValueCountFrequency (%)
99.9 2637
8.8%
71.6 1
 
< 0.1%
71.5 1
 
< 0.1%
71.4 1
 
< 0.1%
71.3 1
 
< 0.1%
71.2 1
 
< 0.1%
70.6 2
 
< 0.1%
70.3 1
 
< 0.1%
70.2 1
 
< 0.1%
69.9 1
 
< 0.1%

Series_Complete_Pop_Pct_US
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct730
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.073242
Minimum0
Maximum100
Zeros3196
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:02.104919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.9
median8
Q325.9
95-th percentile99.9
Maximum100
Range100
Interquartile range (IQR)24

Descriptive statistics

Standard deviation30.155867
Coefficient of variation (CV)1.366173
Kurtosis1.4027356
Mean22.073242
Median Absolute Deviation (MAD)7.8
Skewness1.6128103
Sum659680.9
Variance909.37634
MonotonicityNot monotonic
2023-09-18T22:16:02.418388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3196
 
10.7%
0.7 1226
 
4.1%
99.9 879
 
2.9%
100 879
 
2.9%
0.1 804
 
2.7%
0.3 742
 
2.5%
99.3 637
 
2.1%
2.4 626
 
2.1%
4.9 618
 
2.1%
0.2 575
 
1.9%
Other values (720) 19704
65.9%
ValueCountFrequency (%)
0 3196
10.7%
0.1 804
 
2.7%
0.2 575
 
1.9%
0.3 742
 
2.5%
0.4 129
 
0.4%
0.5 78
 
0.3%
0.6 188
 
0.6%
0.7 1226
 
4.1%
0.8 132
 
0.4%
0.9 58
 
0.2%
ValueCountFrequency (%)
100 879
2.9%
99.9 879
2.9%
99.3 637
2.1%
99.2 108
 
0.4%
99.1 34
 
0.1%
99 18
 
0.1%
98.9 12
 
< 0.1%
98.8 11
 
< 0.1%
98.7 7
 
< 0.1%
98.6 3
 
< 0.1%

Booster_Doses_Vax_pct_agegroup
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct767
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.204858
Minimum0
Maximum76.6
Zeros11220
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:02.723278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.9
Q347.8
95-th percentile69.4
Maximum76.6
Range76.6
Interquartile range (IQR)47.8

Descriptive statistics

Standard deviation25.088819
Coefficient of variation (CV)1.03652
Kurtosis-1.2949944
Mean24.204858
Median Absolute Deviation (MAD)15.9
Skewness0.44827122
Sum723386.4
Variance629.44886
MonotonicityNot monotonic
2023-09-18T22:16:03.181901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11220
37.5%
0.1 212
 
0.7%
14.4 134
 
0.4%
51.2 121
 
0.4%
49.2 119
 
0.4%
48.1 116
 
0.4%
49 114
 
0.4%
51.1 103
 
0.3%
49.1 103
 
0.3%
51.3 102
 
0.3%
Other values (757) 17542
58.7%
ValueCountFrequency (%)
0 11220
37.5%
0.1 212
 
0.7%
0.2 89
 
0.3%
0.3 79
 
0.3%
0.4 63
 
0.2%
0.5 55
 
0.2%
0.6 60
 
0.2%
0.7 54
 
0.2%
0.8 69
 
0.2%
0.9 61
 
0.2%
ValueCountFrequency (%)
76.6 14
 
< 0.1%
76.5 58
0.2%
76.4 36
0.1%
76.3 18
 
0.1%
76.2 17
 
0.1%
76.1 9
 
< 0.1%
76 8
 
< 0.1%
75.9 9
 
< 0.1%
75.8 7
 
< 0.1%
75.7 6
 
< 0.1%

Booster_Doses_Pop_Pct_known
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct585
Distinct (%)2.0%
Missing879
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean15.054973
Minimum0
Maximum99.9
Zeros11384
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:03.696176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.7
Q315.4
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation27.010827
Coefficient of variation (CV)1.7941465
Kurtosis3.8421144
Mean15.054973
Median Absolute Deviation (MAD)1.7
Skewness2.1933752
Sum436699.6
Variance729.58476
MonotonicityNot monotonic
2023-09-18T22:16:04.228465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11384
38.1%
99.9 1908
 
6.4%
0.2 781
 
2.6%
6.2 551
 
1.8%
0.7 534
 
1.8%
3.2 492
 
1.6%
3.1 482
 
1.6%
0.3 476
 
1.6%
8.3 416
 
1.4%
1.6 370
 
1.2%
Other values (575) 11613
38.9%
(Missing) 879
 
2.9%
ValueCountFrequency (%)
0 11384
38.1%
0.1 207
 
0.7%
0.2 781
 
2.6%
0.3 476
 
1.6%
0.4 23
 
0.1%
0.5 30
 
0.1%
0.6 31
 
0.1%
0.7 534
 
1.8%
0.8 147
 
0.5%
0.9 24
 
0.1%
ValueCountFrequency (%)
99.9 1908
6.4%
71.1 2
 
< 0.1%
70.8 1
 
< 0.1%
70.7 1
 
< 0.1%
70.6 1
 
< 0.1%
70.4 3
 
< 0.1%
70.3 3
 
< 0.1%
70.2 11
 
< 0.1%
70.1 3
 
< 0.1%
70 11
 
< 0.1%

Booster_Doses_Vax_Pct_US
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct672
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.583216
Minimum0
Maximum100
Zeros5184
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:04.762756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median9
Q327.7
95-th percentile99.9
Maximum100
Range100
Interquartile range (IQR)26.9

Descriptive statistics

Standard deviation31.907931
Coefficient of variation (CV)1.3529932
Kurtosis1.0468909
Mean23.583216
Median Absolute Deviation (MAD)9
Skewness1.5320951
Sum704808
Variance1018.1161
MonotonicityNot monotonic
2023-09-18T22:16:05.238654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5184
 
17.3%
99.9 2637
 
8.8%
0.3 936
 
3.1%
100 879
 
2.9%
3.7 623
 
2.1%
2.4 618
 
2.1%
6.8 612
 
2.0%
0.8 570
 
1.9%
0.7 545
 
1.8%
2.5 492
 
1.6%
Other values (662) 16790
56.2%
ValueCountFrequency (%)
0 5184
17.3%
0.1 116
 
0.4%
0.2 109
 
0.4%
0.3 936
 
3.1%
0.4 113
 
0.4%
0.5 63
 
0.2%
0.6 1
 
< 0.1%
0.7 545
 
1.8%
0.8 570
 
1.9%
0.9 407
 
1.4%
ValueCountFrequency (%)
100 879
 
2.9%
99.9 2637
8.8%
71.6 1
 
< 0.1%
71.5 1
 
< 0.1%
71.4 1
 
< 0.1%
71.3 2
 
< 0.1%
70.7 1
 
< 0.1%
70.6 1
 
< 0.1%
70.3 1
 
< 0.1%
70.2 1
 
< 0.1%

Booster_Doses_Pop_Pct_known_Last14Days
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct678
Distinct (%)2.3%
Missing879
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean15.205633
Minimum0
Maximum99.9
Zeros11041
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:05.643446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.6
Q315.4
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation26.406352
Coefficient of variation (CV)1.7366164
Kurtosis4.320972
Mean15.205633
Median Absolute Deviation (MAD)3.6
Skewness2.280626
Sum441069.8
Variance697.29542
MonotonicityNot monotonic
2023-09-18T22:16:06.091921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11041
36.9%
99.9 1908
 
6.4%
0.2 513
 
1.7%
0.1 292
 
1.0%
3.5 196
 
0.7%
3.4 192
 
0.6%
3.7 188
 
0.6%
3.6 176
 
0.6%
3.8 158
 
0.5%
0.4 131
 
0.4%
Other values (668) 14212
47.6%
(Missing) 879
 
2.9%
ValueCountFrequency (%)
0 11041
36.9%
0.1 292
 
1.0%
0.2 513
 
1.7%
0.3 128
 
0.4%
0.4 131
 
0.4%
0.5 130
 
0.4%
0.6 124
 
0.4%
0.7 113
 
0.4%
0.8 79
 
0.3%
0.9 69
 
0.2%
ValueCountFrequency (%)
99.9 1908
6.4%
72.1 2
 
< 0.1%
72 1
 
< 0.1%
71.9 1
 
< 0.1%
71.8 3
 
< 0.1%
71.7 3
 
< 0.1%
71.6 1
 
< 0.1%
71.2 1
 
< 0.1%
71.1 2
 
< 0.1%
70.9 2
 
< 0.1%
Distinct17882
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
0
11107 
2
 
90
3
 
33
1
 
24
4
 
5
Other values (17877)
18627 

Length

Max length11
Median length10
Mean length6.0295791
Min length1

Characters and Unicode

Total characters180200
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17128 ?
Unique (%)57.3%

Sample

1st row801,715
2nd row4
3rd row8,827,397
4th row6,699,407
5th row9,166,466

Common Values

ValueCountFrequency (%)
0 11107
37.2%
2 90
 
0.3%
3 33
 
0.1%
1 24
 
0.1%
4 5
 
< 0.1%
6 3
 
< 0.1%
5,009 2
 
< 0.1%
2,795,540 2
 
< 0.1%
109,586,521 2
 
< 0.1%
217,773 2
 
< 0.1%
Other values (17872) 18616
62.3%

Length

2023-09-18T22:16:06.546873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 11107
37.2%
2 90
 
0.3%
3 33
 
0.1%
1 24
 
0.1%
4 5
 
< 0.1%
6 3
 
< 0.1%
763,047 2
 
< 0.1%
16,278,060 2
 
< 0.1%
110,032,950 2
 
< 0.1%
83,592,219 2
 
< 0.1%
Other values (17872) 18616
62.3%

Most occurring characters

ValueCountFrequency (%)
, 33516
18.6%
0 23757
13.2%
1 18965
10.5%
2 14737
8.2%
3 14416
8.0%
5 12860
 
7.1%
6 12579
 
7.0%
8 12382
 
6.9%
7 12365
 
6.9%
4 12345
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146684
81.4%
Other Punctuation 33516
 
18.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23757
16.2%
1 18965
12.9%
2 14737
10.0%
3 14416
9.8%
5 12860
8.8%
6 12579
8.6%
8 12382
8.4%
7 12365
8.4%
4 12345
8.4%
9 12278
8.4%
Other Punctuation
ValueCountFrequency (%)
, 33516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 33516
18.6%
0 23757
13.2%
1 18965
10.5%
2 14737
8.2%
3 14416
8.0%
5 12860
 
7.1%
6 12579
 
7.0%
8 12382
 
6.9%
7 12365
 
6.9%
4 12345
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 33516
18.6%
0 23757
13.2%
1 18965
10.5%
2 14737
8.2%
3 14416
8.0%
5 12860
 
7.1%
6 12579
 
7.0%
8 12382
 
6.9%
7 12365
 
6.9%
4 12345
 
6.9%
Distinct17129
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
0
11215 
1
 
44
756
 
5
1,127
 
4
8,042
 
4
Other values (17124)
18614 

Length

Max length10
Median length9
Mean length4.452185
Min length1

Characters and Unicode

Total characters133058
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15693 ?
Unique (%)52.5%

Sample

1st row914
2nd row1
3rd row6,400
4th row4,837
5th row7,656

Common Values

ValueCountFrequency (%)
0 11215
37.5%
1 44
 
0.1%
756 5
 
< 0.1%
1,127 4
 
< 0.1%
8,042 4
 
< 0.1%
58,426 4
 
< 0.1%
1,454 4
 
< 0.1%
97,347 4
 
< 0.1%
172 4
 
< 0.1%
67,393 4
 
< 0.1%
Other values (17119) 18594
62.2%

Length

2023-09-18T22:16:06.794600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 11215
37.5%
1 44
 
0.1%
756 5
 
< 0.1%
97,347 4
 
< 0.1%
9,405 4
 
< 0.1%
67,393 4
 
< 0.1%
172 4
 
< 0.1%
735 4
 
< 0.1%
1,454 4
 
< 0.1%
58,426 4
 
< 0.1%
Other values (17119) 18594
62.2%

Most occurring characters

ValueCountFrequency (%)
, 20446
15.4%
0 20020
15.0%
1 14115
10.6%
2 11432
8.6%
3 10333
7.8%
4 10134
7.6%
5 9877
7.4%
7 9403
7.1%
6 9334
7.0%
8 9117
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112612
84.6%
Other Punctuation 20446
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20020
17.8%
1 14115
12.5%
2 11432
10.2%
3 10333
9.2%
4 10134
9.0%
5 9877
8.8%
7 9403
8.3%
6 9334
8.3%
8 9117
8.1%
9 8847
7.9%
Other Punctuation
ValueCountFrequency (%)
, 20446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 133058
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 20446
15.4%
0 20020
15.0%
1 14115
10.6%
2 11432
8.6%
3 10333
7.8%
4 10134
7.6%
5 9877
7.4%
7 9403
7.1%
6 9334
7.0%
8 9117
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133058
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 20446
15.4%
0 20020
15.0%
1 14115
10.6%
2 11432
8.6%
3 10333
7.8%
4 10134
7.6%
5 9877
7.4%
7 9403
7.1%
6 9334
7.0%
8 9117
6.9%

Second_Booster_Vax_pct_agegroup
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct652
Distinct (%)3.4%
Missing10548
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean16.932232
Minimum0
Maximum65.1
Zeros6030
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:07.045504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q336
95-th percentile54.8
Maximum65.1
Range65.1
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.14666
Coefficient of variation (CV)1.2488998
Kurtosis-1.0379762
Mean16.932232
Median Absolute Deviation (MAD)1
Skewness0.76207493
Sum327435.5
Variance447.18124
MonotonicityNot monotonic
2023-09-18T22:16:07.359766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6030
20.2%
0.9 948
 
3.2%
0.8 922
 
3.1%
1 600
 
2.0%
1.1 311
 
1.0%
0.7 309
 
1.0%
0.5 272
 
0.9%
0.6 244
 
0.8%
0.4 205
 
0.7%
1.2 195
 
0.7%
Other values (642) 9302
31.1%
(Missing) 10548
35.3%
ValueCountFrequency (%)
0 6030
20.2%
0.1 52
 
0.2%
0.2 61
 
0.2%
0.3 119
 
0.4%
0.4 205
 
0.7%
0.5 272
 
0.9%
0.6 244
 
0.8%
0.7 309
 
1.0%
0.8 922
 
3.1%
0.9 948
 
3.2%
ValueCountFrequency (%)
65.1 13
< 0.1%
65 10
< 0.1%
64.9 12
< 0.1%
64.8 11
< 0.1%
64.7 10
< 0.1%
64.6 8
< 0.1%
64.5 8
< 0.1%
64.4 6
< 0.1%
64.3 7
< 0.1%
64.2 6
< 0.1%

Second_Booster_Pop_Pct_known
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct541
Distinct (%)2.9%
Missing11427
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean23.491045
Minimum0
Maximum99.9
Zeros5772
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:07.700557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.8
Q338.2
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)38.2

Descriptive statistics

Standard deviation33.265096
Coefficient of variation (CV)1.4160756
Kurtosis0.27760802
Mean23.491045
Median Absolute Deviation (MAD)5.8
Skewness1.2910265
Sum433621.2
Variance1106.5666
MonotonicityNot monotonic
2023-09-18T22:16:07.986732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5772
19.3%
99.9 1905
 
6.4%
0.1 732
 
2.4%
0.2 510
 
1.7%
6.1 430
 
1.4%
2.5 403
 
1.3%
0.6 378
 
1.3%
3.6 317
 
1.1%
6.2 167
 
0.6%
0.4 167
 
0.6%
Other values (531) 7678
25.7%
(Missing) 11427
38.2%
ValueCountFrequency (%)
0 5772
19.3%
0.1 732
 
2.4%
0.2 510
 
1.7%
0.3 35
 
0.1%
0.4 167
 
0.6%
0.5 75
 
0.3%
0.6 378
 
1.3%
1 1
 
< 0.1%
1.2 3
 
< 0.1%
1.3 3
 
< 0.1%
ValueCountFrequency (%)
99.9 1905
6.4%
84.6 1
 
< 0.1%
79.5 1
 
< 0.1%
78.3 1
 
< 0.1%
78 1
 
< 0.1%
77.8 1
 
< 0.1%
77.4 1
 
< 0.1%
77.3 2
 
< 0.1%
77.1 1
 
< 0.1%
76.9 1
 
< 0.1%

Second_Booster_Pop_Pct_US
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct566
Distinct (%)2.9%
Missing10548
Missing (%)35.3%
Infinite0
Infinite (%)0.0%
Mean26.299969
Minimum0
Maximum100
Zeros5772
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:08.308134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.6
Q345.9
95-th percentile99.9
Maximum100
Range100
Interquartile range (IQR)45.9

Descriptive statistics

Standard deviation35.432714
Coefficient of variation (CV)1.3472531
Kurtosis-0.30305852
Mean26.299969
Median Absolute Deviation (MAD)5.6
Skewness1.1007522
Sum508588.8
Variance1255.4772
MonotonicityNot monotonic
2023-09-18T22:16:08.606618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5772
19.3%
99.9 1269
 
4.2%
100 879
 
2.9%
0.1 821
 
2.7%
0.2 431
 
1.4%
0.5 403
 
1.3%
2.3 402
 
1.3%
5.6 400
 
1.3%
3.3 358
 
1.2%
5.5 268
 
0.9%
Other values (556) 8335
27.9%
(Missing) 10548
35.3%
ValueCountFrequency (%)
0 5772
19.3%
0.1 821
 
2.7%
0.2 431
 
1.4%
0.3 94
 
0.3%
0.4 148
 
0.5%
0.5 403
 
1.3%
0.8 1
 
< 0.1%
1 3
 
< 0.1%
1.1 4
 
< 0.1%
1.2 2
 
< 0.1%
ValueCountFrequency (%)
100 879
2.9%
99.9 1269
4.2%
99.8 1
 
< 0.1%
92 1
 
< 0.1%
91.9 15
 
0.1%
91.8 21
 
0.1%
91.7 14
 
< 0.1%
91.6 23
 
0.1%
91.5 12
 
< 0.1%
91.4 46
 
0.2%

Second_Booster_Pop_Pct_known_Last14Days
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct760
Distinct (%)4.1%
Missing11427
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean23.290677
Minimum0
Maximum99.9
Zeros5818
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:08.893307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q343.3
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)43.3

Descriptive statistics

Standard deviation32.881567
Coefficient of variation (CV)1.411791
Kurtosis0.40157645
Mean23.290677
Median Absolute Deviation (MAD)5.5
Skewness1.3193618
Sum429922.6
Variance1081.1975
MonotonicityNot monotonic
2023-09-18T22:16:09.183142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5818
19.5%
99.9 1905
 
6.4%
0.1 699
 
2.3%
0.2 314
 
1.1%
0.3 190
 
0.6%
0.6 178
 
0.6%
2.6 170
 
0.6%
0.4 150
 
0.5%
2.5 123
 
0.4%
0.5 107
 
0.4%
Other values (750) 8805
29.5%
(Missing) 11427
38.2%
ValueCountFrequency (%)
0 5818
19.5%
0.1 699
 
2.3%
0.2 314
 
1.1%
0.3 190
 
0.6%
0.4 150
 
0.5%
0.5 107
 
0.4%
0.6 178
 
0.6%
0.7 70
 
0.2%
0.8 38
 
0.1%
0.9 21
 
0.1%
ValueCountFrequency (%)
99.9 1905
6.4%
84.6 1
 
< 0.1%
79.5 1
 
< 0.1%
78.3 1
 
< 0.1%
78 1
 
< 0.1%
77.8 1
 
< 0.1%
77.3 3
 
< 0.1%
77.1 1
 
< 0.1%
77 1
 
< 0.1%
76.9 7
 
< 0.1%

Second_Booster
Categorical

HIGH CARDINALITY  MISSING 

Distinct12233
Distinct (%)63.3%
Missing10548
Missing (%)35.3%
Memory size233.6 KiB
0
6016 
2
 
16
6
 
13
44
 
7
22
 
7
Other values (12228)
13279 

Length

Max length10
Median length7
Mean length5.5480401
Min length1

Characters and Unicode

Total characters107288
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11308 ?
Unique (%)58.5%

Sample

1st row202,047
2nd row0
3rd row2,027,791
4th row2,053,261
5th row2,727,568

Common Values

ValueCountFrequency (%)
0 6016
20.1%
2 16
 
0.1%
6 13
 
< 0.1%
44 7
 
< 0.1%
22 7
 
< 0.1%
1 7
 
< 0.1%
19 6
 
< 0.1%
26 6
 
< 0.1%
301 5
 
< 0.1%
40 5
 
< 0.1%
Other values (12223) 13250
44.3%
(Missing) 10548
35.3%

Length

2023-09-18T22:16:09.486594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 6016
31.1%
2 16
 
0.1%
6 13
 
0.1%
44 7
 
< 0.1%
22 7
 
< 0.1%
1 7
 
< 0.1%
19 6
 
< 0.1%
26 6
 
< 0.1%
17 5
 
< 0.1%
35 5
 
< 0.1%
Other values (12223) 13250
68.5%

Most occurring characters

ValueCountFrequency (%)
, 18820
17.5%
0 13201
12.3%
1 11452
10.7%
2 10155
9.5%
3 9065
8.4%
6 7764
7.2%
5 7646
7.1%
4 7538
7.0%
9 7430
 
6.9%
8 7166
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88468
82.5%
Other Punctuation 18820
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13201
14.9%
1 11452
12.9%
2 10155
11.5%
3 9065
10.2%
6 7764
8.8%
5 7646
8.6%
4 7538
8.5%
9 7430
8.4%
8 7166
8.1%
7 7051
8.0%
Other Punctuation
ValueCountFrequency (%)
, 18820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 18820
17.5%
0 13201
12.3%
1 11452
10.7%
2 10155
9.5%
3 9065
8.4%
6 7764
7.2%
5 7646
7.1%
4 7538
7.0%
9 7430
 
6.9%
8 7166
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 18820
17.5%
0 13201
12.3%
1 11452
10.7%
2 10155
9.5%
3 9065
8.4%
6 7764
7.2%
5 7646
7.1%
4 7538
7.0%
9 7430
 
6.9%
8 7166
 
6.7%

Second_Booster_Last14Days
Categorical

HIGH CARDINALITY  MISSING 

Distinct11189
Distinct (%)57.9%
Missing10548
Missing (%)35.3%
Memory size233.6 KiB
0
6016 
39
 
16
2
 
16
71
 
15
42
 
14
Other values (11184)
13261 

Length

Max length9
Median length7
Mean length4.3858207
Min length1

Characters and Unicode

Total characters84813
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9709 ?
Unique (%)50.2%

Sample

1st row592
2nd row0
3rd row4,897
4th row4,104
5th row7,417

Common Values

ValueCountFrequency (%)
0 6016
20.1%
39 16
 
0.1%
2 16
 
0.1%
71 15
 
0.1%
42 14
 
< 0.1%
43 14
 
< 0.1%
40 14
 
< 0.1%
4 13
 
< 0.1%
56 13
 
< 0.1%
16 11
 
< 0.1%
Other values (11179) 13196
44.2%
(Missing) 10548
35.3%

Length

2023-09-18T22:16:09.765464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 6016
31.1%
2 16
 
0.1%
39 16
 
0.1%
71 15
 
0.1%
42 14
 
0.1%
43 14
 
0.1%
40 14
 
0.1%
4 13
 
0.1%
56 13
 
0.1%
48 11
 
0.1%
Other values (11179) 13196
68.2%

Most occurring characters

ValueCountFrequency (%)
, 13090
15.4%
0 11502
13.6%
1 9094
10.7%
2 7807
9.2%
3 6916
8.2%
4 6675
7.9%
5 6486
7.6%
6 6109
7.2%
9 5788
6.8%
8 5702
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71723
84.6%
Other Punctuation 13090
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11502
16.0%
1 9094
12.7%
2 7807
10.9%
3 6916
9.6%
4 6675
9.3%
5 6486
9.0%
6 6109
8.5%
9 5788
8.1%
8 5702
8.0%
7 5644
7.9%
Other Punctuation
ValueCountFrequency (%)
, 13090
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 13090
15.4%
0 11502
13.6%
1 9094
10.7%
2 7807
9.2%
3 6916
8.2%
4 6675
7.9%
5 6486
7.6%
6 6109
7.2%
9 5788
6.8%
8 5702
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 13090
15.4%
0 11502
13.6%
1 9094
10.7%
2 7807
9.2%
3 6916
8.2%
4 6675
7.9%
5 6486
7.6%
6 6109
7.2%
9 5788
6.8%
8 5702
6.7%

Bivalent_Booster
Categorical

HIGH CARDINALITY  MISSING 

Distinct7604
Distinct (%)91.9%
Missing21615
Missing (%)72.3%
Memory size233.6 KiB
0
 
252
4
 
9
99
 
5
45
 
5
12
 
5
Other values (7599)
7995 

Length

Max length10
Median length9
Mean length8.216177
Min length1

Characters and Unicode

Total characters67956
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7220 ?
Unique (%)87.3%

Sample

1st row378,599
2nd row0
3rd row4,200,667
4th row3,203,837
5th row3,900,005

Common Values

ValueCountFrequency (%)
0 252
 
0.8%
4 9
 
< 0.1%
99 5
 
< 0.1%
45 5
 
< 0.1%
12 5
 
< 0.1%
81 4
 
< 0.1%
7 4
 
< 0.1%
40 4
 
< 0.1%
56,478,510 4
 
< 0.1%
37 4
 
< 0.1%
Other values (7594) 7975
 
26.7%
(Missing) 21615
72.3%

Length

2023-09-18T22:16:10.034131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 252
 
3.0%
4 9
 
0.1%
99 5
 
0.1%
45 5
 
0.1%
12 5
 
0.1%
81 4
 
< 0.1%
7 4
 
< 0.1%
40 4
 
< 0.1%
56,478,510 4
 
< 0.1%
37 4
 
< 0.1%
Other values (7594) 7975
96.4%

Most occurring characters

ValueCountFrequency (%)
, 13399
19.7%
1 7433
10.9%
2 6079
8.9%
3 5908
8.7%
5 5502
8.1%
4 5390
7.9%
0 5071
 
7.5%
9 4873
 
7.2%
6 4833
 
7.1%
7 4788
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54557
80.3%
Other Punctuation 13399
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7433
13.6%
2 6079
11.1%
3 5908
10.8%
5 5502
10.1%
4 5390
9.9%
0 5071
9.3%
9 4873
8.9%
6 4833
8.9%
7 4788
8.8%
8 4680
8.6%
Other Punctuation
ValueCountFrequency (%)
, 13399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 13399
19.7%
1 7433
10.9%
2 6079
8.9%
3 5908
8.7%
5 5502
8.1%
4 5390
7.9%
0 5071
 
7.5%
9 4873
 
7.2%
6 4833
 
7.1%
7 4788
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 13399
19.7%
1 7433
10.9%
2 6079
8.9%
3 5908
8.7%
5 5502
8.1%
4 5390
7.9%
0 5071
 
7.5%
9 4873
 
7.2%
6 4833
 
7.1%
7 4788
 
7.0%

Bivalent_Booster_Pop_Pct_agegroup
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct417
Distinct (%)6.7%
Missing23631
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean11.364428
Minimum0
Maximum45.2
Zeros260
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:10.294996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q13.2
median8.4
Q315.4
95-th percentile40.3
Maximum45.2
Range45.2
Interquartile range (IQR)12.2

Descriptive statistics

Standard deviation10.915936
Coefficient of variation (CV)0.96053543
Kurtosis1.7394455
Mean11.364428
Median Absolute Deviation (MAD)5.9
Skewness1.4654141
Sum71084.5
Variance119.15766
MonotonicityNot monotonic
2023-09-18T22:16:10.603052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 260
 
0.9%
0.5 106
 
0.4%
0.4 100
 
0.3%
0.6 100
 
0.3%
0.3 98
 
0.3%
0.1 96
 
0.3%
0.2 96
 
0.3%
7.3 61
 
0.2%
7.7 60
 
0.2%
7.2 59
 
0.2%
Other values (407) 5219
 
17.5%
(Missing) 23631
79.1%
ValueCountFrequency (%)
0 260
0.9%
0.1 96
 
0.3%
0.2 96
 
0.3%
0.3 98
 
0.3%
0.4 100
 
0.3%
0.5 106
0.4%
0.6 100
 
0.3%
0.7 25
 
0.1%
0.8 29
 
0.1%
0.9 18
 
0.1%
ValueCountFrequency (%)
45.2 3
< 0.1%
45.1 3
< 0.1%
45 2
 
< 0.1%
44.9 1
 
< 0.1%
44.8 4
< 0.1%
44.7 1
 
< 0.1%
44.6 2
 
< 0.1%
44.5 3
< 0.1%
44.4 4
< 0.1%
44.3 7
< 0.1%

Bivalent_Booster_Pop_Pct_known
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct439
Distinct (%)5.5%
Missing21867
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean21.708455
Minimum0
Maximum99.9
Zeros502
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size233.6 KiB
2023-09-18T22:16:10.889317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.8
median7.6
Q324.6
95-th percentile99.9
Maximum99.9
Range99.9
Interquartile range (IQR)22.8

Descriptive statistics

Standard deviation30.410457
Coefficient of variation (CV)1.4008577
Kurtosis1.4522235
Mean21.708455
Median Absolute Deviation (MAD)7.3
Skewness1.6367218
Sum174080.1
Variance924.7959
MonotonicityNot monotonic
2023-09-18T22:16:11.178520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.9 756
 
2.5%
0 502
 
1.7%
0.2 331
 
1.1%
0.1 266
 
0.9%
3.5 246
 
0.8%
0.3 238
 
0.8%
0.7 188
 
0.6%
2.8 185
 
0.6%
6.3 167
 
0.6%
2.3 142
 
0.5%
Other values (429) 4998
 
16.7%
(Missing) 21867
73.2%
ValueCountFrequency (%)
0 502
1.7%
0.1 266
0.9%
0.2 331
1.1%
0.3 238
0.8%
0.4 16
 
0.1%
0.5 22
 
0.1%
0.6 48
 
0.2%
0.7 188
 
0.6%
0.8 49
 
0.2%
0.9 26
 
0.1%
ValueCountFrequency (%)
99.9 756
2.5%
73.5 1
 
< 0.1%
73.2 1
 
< 0.1%
73 1
 
< 0.1%
72.9 1
 
< 0.1%
72.6 1
 
< 0.1%
72.5 2
 
< 0.1%
72.4 1
 
< 0.1%
72.3 1
 
< 0.1%
72.2 1
 
< 0.1%

Interactions

2023-09-18T22:15:48.614183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:34.855292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:39.121727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:44.509342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:48.803025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:52.806298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:58.816266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:04.071411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:09.573752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:14.311577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:19.220628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:25.175478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:29.676469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:33.993800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:39.933029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:44.254567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:48.863581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:35.117478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:39.390117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:44.758696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:49.049477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:53.157171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:59.110734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:04.357353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:09.980737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:14.592467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:19.518712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:25.732910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:29.957280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:34.665891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:40.202350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:44.554865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:49.223143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:35.361593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:39.705526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:45.001779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:49.332812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:53.515043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:59.382431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:04.620759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:10.344366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:14.875148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:19.789348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:25.991359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:30.231883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:34.904997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:40.483626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:44.849473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:49.675203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:35.608036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:40.087015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:45.244676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:49.560957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:53.955397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:59.739581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:04.959372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:10.760781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:15.158544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:20.046167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:26.241157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:30.493599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:35.213129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:40.730512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:45.108218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:50.138592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:35.884174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:40.468675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:45.498115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:49.794586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:54.336730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:00.133224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:05.430957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:11.160215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:15.465419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:20.299812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:26.482448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:30.755166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:35.573579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:40.961702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:45.404430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:50.591390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:36.130066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:40.859614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:45.749534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:50.047444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:55.036361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:00.856437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:05.774200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:11.407835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:16.122340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:20.562919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:26.749436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:31.024653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:35.930619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:41.216383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:45.659741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:51.030866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:36.377036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:41.252195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:45.996548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:50.293842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:55.586028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:01.097875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:06.050896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:11.663678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:16.386198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:20.812228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:26.996408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:31.278416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:36.255741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:41.472343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:45.899626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:51.782208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:36.645751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:41.674962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:46.273206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:50.554221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:55.987668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:01.376432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:06.332922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:11.921499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:16.664084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:21.090481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:27.268616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:31.559346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:36.693573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:41.751199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:46.167412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:52.133099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:36.921489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:42.073734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:46.535969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:50.809880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:56.460770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:01.663883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:06.610358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:12.181167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:16.939745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:21.452527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:27.521620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:31.802221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:37.071607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:41.990865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:46.428846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:52.497642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:37.159603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:42.463418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:46.793055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:51.058956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:56.841391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:01.961147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:06.889929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:12.438527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:17.232907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:21.850969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:27.788136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:32.080342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:37.473729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:42.248293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:46.686371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:52.879789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:37.415015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:42.872770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:47.052715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:51.321374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:57.175433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:02.254159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:07.176827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:12.698511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:17.535638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:22.242051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:28.036157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:32.377429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:37.863268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:42.558069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:46.936707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:53.155380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:37.680723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:43.252307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:47.335314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:51.573001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:57.484843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:02.539208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:07.573043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:12.949545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:17.828079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:22.645535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:28.294129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:32.686209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:38.302209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:42.855045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:47.213634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:53.430830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:37.942398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:43.508802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:47.594472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:51.804925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:57.749748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:02.879810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:07.938514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:13.211771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:18.116589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:23.068637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:28.550396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:32.989738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:38.789321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:43.140813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:47.480422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:53.703816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:38.180554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:43.757612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:47.850741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:52.064230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:58.005808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:03.170033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:08.302781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:13.472239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:18.410639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:23.516961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:28.809309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:33.249526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:39.076721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:43.444869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:47.771902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:53.952995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:38.426867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:44.001201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:48.308826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:52.293015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:58.253215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:03.468887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:08.746450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:13.744417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:18.682224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:23.923460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:29.078712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:33.495978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:39.375630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:43.715630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:48.063102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:54.207230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:38.844347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:44.255456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:48.545534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:52.543389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:14:58.539409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:03.746946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:09.144364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:14.006721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:18.933556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:24.434386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:29.380977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:33.744301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:39.660479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:43.978503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T22:15:48.333540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-18T22:16:11.453072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Administered_Dose1_pct_knownAdministered_Dose1_pct_USAdministered_Dose1_pct_agegroupSeries_Complete_Pop_pct_agegroupSeries_Complete_Pop_Pct_knownSeries_Complete_Pop_Pct_USBooster_Doses_Vax_pct_agegroupBooster_Doses_Pop_Pct_knownBooster_Doses_Vax_Pct_USBooster_Doses_Pop_Pct_known_Last14DaysSecond_Booster_Vax_pct_agegroupSecond_Booster_Pop_Pct_knownSecond_Booster_Pop_Pct_USSecond_Booster_Pop_Pct_known_Last14DaysBivalent_Booster_Pop_Pct_agegroupBivalent_Booster_Pop_Pct_knownDemographic_category
Administered_Dose1_pct_known1.0000.9970.5440.5240.9970.9940.3260.5990.9810.5790.1030.5150.5130.5150.3700.9510.806
Administered_Dose1_pct_US0.9971.0000.5510.5320.9940.9960.3170.5990.9790.5810.1020.5160.5800.5160.3700.9460.884
Administered_Dose1_pct_agegroup0.5440.5511.0000.9930.5540.5630.7430.7410.5380.6550.5740.5850.5260.5900.5850.6090.512
Series_Complete_Pop_pct_agegroup0.5240.5320.9931.0000.5380.5460.7680.7520.5260.6630.6060.5920.5290.5960.6360.6370.534
Series_Complete_Pop_Pct_known0.9970.9940.5540.5381.0000.9970.3390.6050.9860.5830.1110.5180.5160.5180.3990.9580.800
Series_Complete_Pop_Pct_US0.9940.9960.5630.5460.9971.0000.3360.6100.9820.5880.1160.5290.5910.5310.3910.9580.871
Booster_Doses_Vax_pct_agegroup0.3260.3170.7430.7680.3390.3361.0000.8310.3540.7870.8950.7300.6630.7310.7920.6390.493
Booster_Doses_Pop_Pct_known0.5990.5990.7410.7520.6050.6100.8311.0000.6190.9690.6880.9930.9920.9930.5030.9900.733
Booster_Doses_Vax_Pct_US0.9810.9790.5380.5260.9860.9820.3540.6191.0000.6000.1060.5190.5810.5190.3960.9580.801
Booster_Doses_Pop_Pct_known_Last14Days0.5790.5810.6550.6630.5830.5880.7870.9690.6001.0000.6800.9810.9800.9830.2260.8670.576
Second_Booster_Vax_pct_agegroup0.1030.1020.5740.6060.1110.1160.8950.6880.1060.6801.0000.6990.6420.6990.8760.3720.203
Second_Booster_Pop_Pct_known0.5150.5160.5850.5920.5180.5290.7300.9930.5190.9810.6991.0000.9990.9980.2270.9920.685
Second_Booster_Pop_Pct_US0.5130.5800.5260.5290.5160.5910.6630.9920.5810.9800.6420.9991.0000.9970.1870.9890.661
Second_Booster_Pop_Pct_known_Last14Days0.5150.5160.5900.5960.5180.5310.7310.9930.5190.9830.6990.9980.9971.0000.2300.9880.599
Bivalent_Booster_Pop_Pct_agegroup0.3700.3700.5850.6360.3990.3910.7920.5030.3960.2260.8760.2270.1870.2301.0000.5750.529
Bivalent_Booster_Pop_Pct_known0.9510.9460.6090.6370.9580.9580.6390.9900.9580.8670.3720.9920.9890.9880.5751.0000.851
Demographic_category0.8060.8840.5120.5340.8000.8710.4930.7330.8010.5760.2030.6850.6610.5990.5290.8511.000

Missing values

2023-09-18T22:15:54.907733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-18T22:15:55.779568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-18T22:15:56.608840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateDemographic_categoryAdministered_Dose1Administered_Dose1_pct_knownAdministered_Dose1_pct_USSeries_Complete_YesAdministered_Dose1_pct_agegroupSeries_Complete_Pop_pct_agegroupSeries_Complete_Pop_Pct_knownSeries_Complete_Pop_Pct_USBooster_Doses_Vax_pct_agegroupBooster_Doses_Pop_Pct_knownBooster_Doses_Vax_Pct_USBooster_Doses_Pop_Pct_known_Last14DaysBooster_Doses_YesBooster_Doses_Yes_Last14DaysSecond_Booster_Vax_pct_agegroupSecond_Booster_Pop_Pct_knownSecond_Booster_Pop_Pct_USSecond_Booster_Pop_Pct_known_Last14DaysSecond_BoosterSecond_Booster_Last14DaysBivalent_BoosterBivalent_Booster_Pop_Pct_agegroupBivalent_Booster_Pop_Pct_known
05/10/23Race_eth_NHAIAN1,911,8550.90.71,588,65378.565.20.90.750.50.80.91.4801,71591450.10.60.50.9202,047592378,59915.50.7
15/10/23Age_unknown9,3440.00.02,491NaNNaN0.00.00.20.00.00.0410.00.00.00.0000NaN0.0
25/10/23Race_eth_NHAsian13,983,7046.85.212,609,00073.666.46.95.570.08.36.99.68,827,3976,40056.26.15.57.12,027,7914,8974,200,66722.18.3
35/10/23Race_eth_NHMult_Oth12,665,1036.24.711,389,487NaNNaN6.24.958.86.36.27.36,699,4074,83754.96.15.66.02,053,2614,1043,203,837NaN6.3
45/10/23Race_eth_NHBlack21,157,65410.37.818,545,87051.345.010.18.049.48.610.111.59,166,4667,65647.98.27.410.82,727,5687,4173,900,0059.57.7
55/10/23Sex_Male128,162,11847.947.4109,144,88278.466.847.647.349.245.547.647.353,713,82235,82352.244.744.747.416,426,58635,84925,530,53015.645.3
65/10/23Race_eth_NHNHOPI666,4320.30.2598,99271.864.50.30.352.00.30.30.4311,33523449.70.20.20.369,896181117,60412.70.2
75/10/23Ages_25-39_yrs57,069,47121.121.147,567,70983.469.620.620.640.116.120.612.319,094,0019,317NaNNaNNaNNaNNaNNaN7,348,11210.713.0
85/10/23Ages_40-49_yrs36,300,94413.413.431,261,42889.076.613.613.648.112.713.67.315,049,2815,562NaNNaNNaNNaNNaNNaN5,856,01114.410.4
95/10/23Ages_65-74_yrs34,959,72712.912.930,816,76295.095.013.413.472.118.813.419.922,227,92015,10958.935.635.641.713,094,83731,55013,352,33741.923.6
DateDemographic_categoryAdministered_Dose1Administered_Dose1_pct_knownAdministered_Dose1_pct_USSeries_Complete_YesAdministered_Dose1_pct_agegroupSeries_Complete_Pop_pct_agegroupSeries_Complete_Pop_Pct_knownSeries_Complete_Pop_Pct_USBooster_Doses_Vax_pct_agegroupBooster_Doses_Pop_Pct_knownBooster_Doses_Vax_Pct_USBooster_Doses_Pop_Pct_known_Last14DaysBooster_Doses_YesBooster_Doses_Yes_Last14DaysSecond_Booster_Vax_pct_agegroupSecond_Booster_Pop_Pct_knownSecond_Booster_Pop_Pct_USSecond_Booster_Pop_Pct_known_Last14DaysSecond_BoosterSecond_Booster_Last14DaysBivalent_BoosterBivalent_Booster_Pop_Pct_agegroupBivalent_Booster_Pop_Pct_known
2987612/13/20Race_eth_NHMultiracial4371.51.2990.00.01.31.00.00.01.30.0000.00.00.00.000NaNNaNNaN
2987712/13/20Sex_known36,53499.999.29,577NaNNaN99.999.00.00.099.90.0000.00.00.00.000NaNNaNNaN
2987812/13/20Race_eth_Hispanic2,6829.37.37650.00.010.27.90.00.010.10.0000.00.00.00.000NaNNaNNaN
2987912/13/20Ages_16-17_yrs3180.90.9690.00.00.70.70.00.00.00.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
2988012/13/20Race_eth_NHMult_Oth1,2314.33.3388NaNNaN5.24.00.00.05.20.0000.00.00.00.000NaNNaNNaN
2988112/13/20Age_known36,81199.999.99,669NaNNaN99.999.90.00.099.90.0000.00.00.00.000NaNNaNNaN
2988212/13/20Ages_18-24_yrs1,8435.05.05090.00.05.35.30.00.05.30.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
2988312/13/20Ages_65+_yrs10,75229.229.22,6870.00.027.827.80.00.028.20.0000.00.00.00.000NaNNaNNaN
2988412/13/20Ages_40-49_yrs5,51715.015.01,4830.00.015.315.30.00.015.60.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
2988512/13/20Ages_65-74_yrs6,59117.917.91,9570.00.020.220.20.00.020.50.0000.00.00.00.000NaNNaNNaN